Zero-day malware still slips past the best detection systems because most models need thousands of labeled examples before they learn anything useful. That dependency is exactly the weak point: by the time enough samples accumulate, the damage is already spreading. Traditional few-shot approaches promise quicker adaptation, yet they often reduce rich forensic evidence into flat feature vectors and end up overfitting to byte-level quirks rather than behavioral signals. This work takes a different path and develops a systematic pipeline that treats malware traces as structured evidence, feeding them through a sequence of five meta-learning extensions designed to survive the scarcity of zero-day samples. We begin with EpiForge, which fabricates realistic few-shot episodes from evidence-graphs and injects controlled novelties without breaking causal consistency, ensuring the training tasks resemble true zero-day strangeness. These episodes drive BayesMAML-E, a hierarchical Bayesian meta-learner that encodes evidence-type priors, producing task-conditioned initializations and calibrated uncertainty estimates. The output then flows into CoShaRE, which sparsifies decisions by learning counterfactual Shapley-regularized masks retaining only causally sufficient evidence and generating counter-examples that test decision stability. From there, OptiQuill decides how to spend scarce resources, balancing sandbox runs and labeling efforts using a budget-aware Lagrangian bandit that targets maximum downstream meta-learning gains. Finally, CausalFADE distills the learned behavior into compact automata and executable rules, turning black-box predictions into forensic signatures that analysts can trust and re-use for the process. Across all five stages, we see evidence of measurable impact: 5-shot accuracy improves by more than 10% points over standard MAML, calibration error falls to near 2%, and label and compute budgets are cut substantially. The result is not just faster adaptation but also auditable, causally grounded signatures that close the loop between evidence collection, learning, and deployment. This work appears to offer a path toward zero-day detection that is both technically feasible and operationally sustainable in process.

Urban intersections are responsible for nearly 40% of total vehicular delays in metropolitan networks, making traffic signal optimization a central challenge for sustainable urban mobility. Traditional signal control systems remain constrained by rule-based logic or simple vehicle counting, often failing under heterogeneous and congested traffic conditions common in rapidly developing cities. To address this, the present study introduces a real-time, spatial occupancy–driven framework for adaptive green signal prediction built upon a novel hybrid two-stream detection architecture integrated with a visibility-aware fusion mechanism. The proposed model combines anchor-based and anchor-free detection paradigms through a learnable gating layer, enabling accurate recognition of vehicles of varying scales and orientations even under partial occlusion. A dedicated visibility head further refines confidence estimation, enhancing detection reliability in dense urban scenes. Experiments conducted on real-world intersection video data demonstrate that the framework achieves an mAP@0.5 of 93.7%, a 37% reduction in spatial occupancy estimation error, and a 40% decrease in green signal prediction error compared to baseline systems, while sustaining a real-time throughput of 64.7 FPS and an end-to-end perception-to-prediction latency of 61 ms, confirming its viability for real-time traffic control. Beyond practical deployment, this work contributes a novel methodological bridge between computer vision and intelligent transportation systems, offering the research community a benchmark framework for integrating perception-aware learning into traffic management, signal optimization, and broader smart-city decision infrastructures. Clinical Trial Not Applicable.

The increasing number of automobiles on the road has worsened traffic, raised questions about safety, and had an adverse effect on the environment. Traffic Signal Control (TSC) has become a key strategy to address these issues. While conventional TSC systems help manage urban traffic, their reliance on fixed schedules and simplified models limits their effectiveness in complex, real-world conditions. In order to overcome the above shortcomings, this paper introduces an Improved Twin-Actor Twin-Delayed Deep Deterministic Policy Gradient (ITATD3) algorithm designed for traffic signal control at single intersections. The main innovation of the ITATD3 proposed is in two aspects: (1) the incorporation of a spatial occupancy-based reward which in combination with waiting time, better measures road-space utilization and congestion than traditional metrics, and (2) the application of the Cheetah Optimization Algorithm (COA) for fine-tuning of key hyper parameters of the ITATD3 model to improve convergence speed and stability. The performance of the proposed model is compared against four benchmark models, like TATD3, TD3, D3QN, and DQN. Results show that ITATD3 performs consistently better than other models on various performance metrics, such as travel delay, average waiting time, traffic conflicts, spatial occupancy, throughput, and average travel time. The proposed ITATD3 reduced travel delay by a maximum of 68% and improved traffic throughput significantly compared to conventional models. These results reveal the benefits of ITATD3 as a viable solution for real-time data-driven traffic signal control in intelligent transportation systems.

The complexity of traffic flow patterns significant challenges in predicting traffic green signal timings using conventional methods. Most of conventional methods relied on vehicle counts and speeds. These methods often did not consider crucial factors such as Spatial Occupancy, long-term dependencies, and the non-linear relationships. Recent advancements in Convolutional Neural Networks (CNNs) have enabled better capturing of patterns in traffic data. These advancements are essential for effectively predicting vehicle Green Signal Time by considering accurate detection and tracking, Spatial Occupancy calculation, long-term dependencies, and non-linear relationships in traffic data. The PVD-GSTPS framework has been proposed as an innovative solution for predicting vehicle Green Signal Time with the help of advanced CNN. This framework leverages the capabilities of two fine-tuned object detection models YOLO v8 and Faster R-CNN for precise vehicle detection, while a Byte Sort Tracker monitors the trajectories of detected vehicles. Additionally, a vehicle counting module assesses the number of vehicles in specified areas, and a size assignment process estimates Green Signal Time based on Spatial Occupancy calculations. This study is limited by the fixed duration of the QMUL video dataset utilized. This restricts data availability and complicates the establishment of strong correlations between Green Signal Time and Spatial Occupancy. To mitigate this issue, we utilized a Generative Adversarial Network (GAN) to generate realistic synthetic data. Long Short-Term Memory (LSTM) networks and polynomial regression techniques are utilized to capture the relationships within this dataset. In this study, we used the QMUL dataset to validate our hypothesis. The results demonstrate that our PVD-GSTPS framework significantly outperforms Enhanced YOLO v8, Original YOLO v8, and Faster R-CNN.

The number of vehicles on the road continues to rise due to population growth, economic development, urbanization, technological advancements, and improved infrastructure. The result is that traffic congestion, delays, and emissions are on the rise in cities. A deep learning-based technology called Traffic signal control system (TSCS) is being used to deal with these issues at intersections. Through the TSCS, accidents, frustrations, and impatience can be reduced by efficiently regulating traffic flow. A TSCS's effectiveness in terms of green signal time is determined by both vehicle counts and the amount of space vehicles occupy. Traffic counting and spatial occupancy are calculated accurately and quickly with accurate and fast vehicle detection and tracking. T his study presents a framework for calculating spatial occupancy as well as a comparative analysis of vehicle counting in each cycle. Two trackers are used in this framework: traditional Euclidean distance and deep sort, which are designed to count different types of vehicles in complex environments like QMUL junctions, along with modified Yolo v3. Our experiment showed that deep sort performs far better than the traditional Euclidian distance tracker. The MAE of the modified Yolo v3 with deep sort is 3.0, whereas the MAE of the traditional Euclidian distance tracker is 11.8. The results of this analysis study may result in an improvement in traffic management and congestion control in the future.

The devastating impact of floods could result in long-term economic consequences, property damage, mortality, and disruptions to essential services. Flood risk assessment is crucial to the solution of these problems because it provides valuable information about the potential frequency and magnitude of flooding episodes. Through a rigorous examination of variables such as geography, hydrology, land use, and climatic changes, flood risk assessment determines how susceptible populations and assets are to flooding. This method assists in locating areas that are vulnerable to flooding. With this information at hand, policymakers may improve catastrophe planning and response procedures, create informed plans for reducing the effects of floods, and allocate resources more effectively to reduce their effects. In the proposed method, the otsu and difference thresholding methods are being used to calculate the population affected by the flood. The model is 92.3% accurate in predicting the locations of the 12 affected districts. The resulting flood map may then be coupled with demographic statistics.

Real-time traffic management systems are needed to manage urbanization’s impact on traffic conditions. Traffic dynamics in cities are complex, and traditional signal timing methods and simple vehicle detection cannot handle them. The paper describes a method for improving urban traffic studies using Real-time Dense Analysis and Management using Visual Graph Networks (RDAMVGN) that utilizes deep learning techniques along with Visual Graph Networks based on visualizations. This study aims to develop a robust, dynamic, and accurate approach to traffic density analysis, vehicle classification, and dynamic signal control in order to achieve high accuracy in traffic flow analysis. The proposed RDAMVGN framework incorporates both a LACF-YOLO detection model and a Faster Region-Based Convolutional Neural Network (Faster RCNN) detection model for high-speed and high-accuracy vehicle identification. This framework applies transfer learning to adapt pre-trained features to new traffic environments. This enhances vehicle classification in complex urban scenes and improves the model’s ability to distinguish vehicles from non-vehicle objects. Traffic flow optimization is achieved by using Mask RCNN and LSTM. The comparative analysis includes Fine-Tuned YOLOv8 with Fine-Tuned Faster RCNN, Fine-Tuned YOLOv3 with Fine-Tuned Faster RCNN, YOLOv5 with Faster RCNN, YOLOv3 with Faster RCNN, standalone Faster RCNN, YOLOv5, YOLOv3, Reinforcement Learning (RL), and Deep Reinforcement Learning (DRL) models, evaluate across precision, recall, accuracy, under pre-emption and non-pre-emption scenarios. The RDAMVGN-based detection component exhibits demonstrably superior performance across all evaluation metrics in real-time traffic management systems. It achieves a high precision of 97.75 %, indicating accurate vehicle identification with minimal false positives. Its recall rate of 96.17 % reflects strong detection capability, minimizing missed vehicles. The overall accuracy stands at 98.48 %, indicating robust classification and localization. In pre-emption scenario, the model maintains its lead with pre-emption precision of 94.79 %, pre-emption recall of 98.66 %, and pre-emption accuracy of 97.43 %, showcasing its reliability in real-time traffic prioritization scenarios.

Currently, adversarial attacks are typically designed for specific tasks, and although there are some task-agnostic attacks are generally less effective than task-specific ones. These attacks exploit the fact that CNN-based feature extractors cannot be reversed or inverted, making the downstream models vulnerable to these attacks. However, they are not optimally designed because they use the entire CNN to generate an adversarial example. This paper proposes a modified version of this approach called Faster Mimic and Fool (MaF), which requires less time and fewer resources to create an adversarial image. The experiment involved selecting 100 random FlickR 8K images and testing the attack on an Inception-V3-based captioning model. The results showed that Faster MaF achieved a Bleu-4 score that is 13.5% and 31.1% better than MaF and OIMO, respectively. Since Faster MaF requires knowledge of the CNN, it can be considered a grey-box attack.

Traffic congestion is a serious challenge in urban areas. So, to address this challenge, the intelligent traffic management system (ITMS) is used to manage traffic on road networks. Managing traffic helps to focus on environmental impacts as well as emergency situations. However, the ITMS system has many challenges in analyzing scenes of complex traffic. New technologies such as computer vision (CV) and artificial intelligence (AI) are being used to solve these challenges. As a result, these technologies have made a distinct identity in the surveillance industry, particularly when it comes to keeping a constant eye on traffic scenes. There are many vehicle attributes and existing approaches that are being used in the development of ITMS, along with imaging technologies. In this paper, we reviewed the ITMS-based components that describe existing imaging technologies and existing approaches on the basis of their need for developing ITMS. The first component describes the traffic scene and imaging technologies. The second component talks about vehicle attributes and their utilization in existing vehicle-based approaches. The third component explains the vehicle’s behavior on the basis of the second component’s outcome. The fourth component explains how traffic-related applications can assist in the management and monitoring of traffic flow, as well as in the reduction of congestion and the enhancement of road safety. The fifth component describes the different types of ITMS applications. The sixth component discusses the existing methods of traffic signal control systems (TSCSs). Aside from these components, we also discuss existing vehicle-related tools such as simulators that work to create realistic traffic scenes. In the last section named discussion, we discuss the future development of ITMS and draw some conclusions. The main objective of this paper is to discuss the possible solutions to different problems during the development of ITMS in one place, with the help of components that would play an important role for an ITMS developer to achieve the goal of developing efficient ITMS.

In the view of large number of vehicles on the road, manually controlling vehicles become very difficult and automated traffic management system is a need. Vehicle detection is necessary for automatic traffic management to accurately monitor and control the flow of traffic. In this paper we propose an improved YOLOv5s model for vehicle detection. It uses GhostConv and Ghostbottleneck as part of the architecture. One of the main benefits of this model is that it reduces the number of parameters, which can lead to faster training and inference times. Adapting the GhostConv and GhostBottleneck in model, reduces the amount of calculation, which makes the model faster. For training of models we make a traffic vehicles dataset that consist of four classes: auto, bike, car and bus. The experimental results show that the new model is able to reduce the number of parameters generated by 61.6% which also reduces the computation time of model. This model is also able to perform better as compared to original YOLOV5s.